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Gil-Gomez A, Rest JS. Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes. Mol Biol Evol 2024; 41:msae098. [PMID: 38768245 PMCID: PMC11136681 DOI: 10.1093/molbev/msae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/14/2024] [Accepted: 05/15/2024] [Indexed: 05/22/2024] Open
Abstract
As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.
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Affiliation(s)
- Alejandro Gil-Gomez
- Department of Ecology and Evolution, Laufer Center for Physical and Quantitative Biology, Stony Brook University, 650 Life Sciences, Stony Brook, NY 11794-4254, USA
| | - Joshua S Rest
- Department of Ecology and Evolution, Laufer Center for Physical and Quantitative Biology, Stony Brook University, 650 Life Sciences, Stony Brook, NY 11794-4254, USA
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Wollenberg Valero KC. Brief Communication: The Predictable Network Topology of Evolutionary Genomic Constraint. Mol Biol Evol 2024; 41:msae033. [PMID: 38366776 PMCID: PMC10906983 DOI: 10.1093/molbev/msae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 01/03/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024] Open
Abstract
Large-scale comparative genomics studies offer valuable resources for understanding both functional and evolutionary rate constraints. It is suggested that constraint aligns with the topology of genomic networks, increasing toward the center, with intermediate nodes combining relaxed constraint with higher contributions to the phenotype due to pleiotropy. However, this pattern has yet to be demonstrated in vertebrates. This study shows that constraint intensifies toward the network's center in placental mammals. Genes with rate changes associated with emergence of hibernation cluster mostly toward intermediate positions, with higher constraint in faster-evolving genes, which is indicative of a "sweet spot" for adaptation. If this trend holds universally, network node metrics could predict high-constraint regions even in clades lacking empirical constraint data.
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Whiting JR, Paris JR, van der Zee MJ, Fraser BA. AF‐vapeR
: A multivariate genome scan for detecting parallel evolution using allele frequency change vectors. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- James R. Whiting
- Department of Biosciences University of Exeter Exeter UK
- Department of Biological Sciences University of Calgary Calgary Alberta Canada
| | - Josephine R. Paris
- Department of Biosciences University of Exeter Exeter UK
- Department of Health, Life and Environmental Sciences University of L'Aquila L'Aquila Italy
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Wollenberg Valero KC, Garcia-Porta J, Irisarri I, Feugere L, Bates A, Kirchhof S, Jovanović Glavaš O, Pafilis P, Samuel SF, Müller J, Vences M, Turner AP, Beltran-Alvarez P, Storey KB. Functional genomics of abiotic environmental adaptation in lacertid lizards and other vertebrates. J Anim Ecol 2021; 91:1163-1179. [PMID: 34695234 DOI: 10.1111/1365-2656.13617] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 09/27/2021] [Indexed: 11/27/2022]
Abstract
Understanding the genomic basis of adaptation to different abiotic environments is important in the context of climate change and resulting short-term environmental fluctuations. Using functional and comparative genomics approaches, we here investigated whether signatures of genomic adaptation to a set of environmental parameters are concentrated in specific subsets of genes and functions in lacertid lizards and other vertebrates. We first identify 200 genes with signatures of positive diversifying selection from transcriptomes of 24 species of lacertid lizards and demonstrate their involvement in physiological and morphological adaptations to climate. To understand how functionally similar these genes are to previously predicted candidate functions for climate adaptation and to compare them with other vertebrate species, we then performed a meta-analysis of 1,100 genes under selection obtained from -omics studies in vertebrate species adapted to different abiotic factors. We found that the vertebrate gene set formed a tightly connected interactome, which was to 23% enriched in previously predicted functions of adaptation to climate, and to a large part (18%) involved in organismal stress response. We found a much higher degree of identical genes being repeatedly selected among different animal groups (43.6%), and of functional similarity and post-translational modifications than expected by chance, and no clear functional division between genes used for ectotherm and endotherm physiological strategies. In total, 171 out of 200 genes of Lacertidae were part of this network. These results highlight an important role of a comparatively small set of genes and their functions in environmental adaptation and narrow the set of candidate pathways and markers to be used in future research on adaptation and stress response related to climate change.
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Affiliation(s)
| | - Joan Garcia-Porta
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - Iker Irisarri
- Department of Applied Bioinformatics, Institute for Microbiology and Genetics, University of Göttingen, Göttingen, Germany.,Campus Institut Data Science (CIDAS), Göttingen, Germany
| | - Lauric Feugere
- Department of Biological and Marine Sciences, University of Hull, Kingston-Upon-Hull, UK
| | - Adam Bates
- Department of Biological and Marine Sciences, University of Hull, Kingston-Upon-Hull, UK
| | - Sebastian Kirchhof
- Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany.,New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | | | - Panayiotis Pafilis
- Section of Zoology and Marine Biology, Department of Biology, National and Kapodistrian University of Athens, Athens, Greece
| | - Sabrina F Samuel
- Department of Biomedical Sciences, University of Hull, Kingston-Upon-Hull, UK
| | - Johannes Müller
- Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - Miguel Vences
- Zoological Institute, Braunschweig University of Technology, Braunschweig, Germany
| | - Alexander P Turner
- Department of Computer Science, University of Nottingham, Nottingham, UK
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Dai A, Wang Y, Greenberg A, Liufu Z, Tang T. Rapid Evolution of Autosomal Binding Sites of the Dosage Compensation Complex in Drosophila melanogaster and Its Association With Transcription Divergence. Front Genet 2021; 12:675027. [PMID: 34194473 PMCID: PMC8238462 DOI: 10.3389/fgene.2021.675027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/19/2021] [Indexed: 11/25/2022] Open
Abstract
How pleiotropy influences evolution of protein sequence remains unclear. The male-specific lethal (MSL) complex in Drosophila mediates dosage compensation by 2-fold upregulation of the X chromosome in males. Nevertheless, several MSL proteins also bind autosomes and likely perform functions not related to dosage compensation. Here, we study the evolution of MOF, MSL1, and MSL2 biding sites in Drosophila melanogaster and its close relative Drosophila simulans. We found pervasive expansion of the MSL binding sites in D. melanogaster, particularly on autosomes. The majority of these newly-bound regions are unlikely to function in dosage compensation and associated with an increase in expression divergence between D. melanogaster and D. simulans. While dosage-compensation related sites show clear signatures of adaptive evolution, these signatures are even more marked among autosomal regions. Our study points to an intriguing avenue of investigation of pleiotropy as a mechanism promoting rapid protein sequence evolution.
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Affiliation(s)
- Aimei Dai
- State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yushuai Wang
- State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | | | - Zhongqi Liufu
- State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Tian Tang
- State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
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